In this literature review, we focus on supervised learning tasks due to similarity with our work.
Currently, machine learning models share knowledge in two ways [1]}, [2]}:
(i) feature-based MTL that distributes knowledge across training representative features, and
(ii) parameter-based MTL that uses the model parameters trained in a specific task to fit the related tasks.
In this work, we are interested in studying the latent space shared among all tasks while focusing on SS as the main task restricted to hourglass models.
